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Introduction to AI Agents - MCP
This lesson introduces the core concepts of AI agents and the Model Context Protocol (MCP). It starts by defining what constitutes a true 'AI Agent' by explaining the 'action-feedback' loop where an LLM directs its own processes, contrasting it with simpler 'agentic workflows' which are deterministically coded. The central focus will be on understanding the 'MxN problem' in AI development (connecting M models to N tools) and how MCP provides a standardized interface, analogous to the Language Server Protocol (LSP), to simplify this complexity to a more manageable M+N integration effort. The lesson concludes by surveying key industrial use cases for AI agents, such as automated coding, advanced research, customer service automation, and the creation of business-specific copilots.
Designing Compute Systems
This lesson provides a foundational overview of the core compute services available on Google Cloud. It will start with a deep dive into Compute Engine (IaaS), covering machine types, persistent disks, and specialized options like Sole-Tenant nodes, Preemptible/Spot VMs, and security-focused Shielded/Confidential VMs. The session will then contrast this with serverless platforms, analyzing the use cases for App Engine (Standard vs. Flexible), Cloud Functions for event-driven triggers, and Cloud Run for serving stateless containers. The focus will be on comparing these services to understand the trade-offs between control, flexibility, and operational overhead.
Model Training & Validation
This lesson introduces the core concepts of model training within the AWS ecosystem. It will cover the distinction between training on a local machine versus leveraging cloud resources like Amazon SageMaker for remote and distributed training. Key topics include understanding data and model parallelism for scaling, and a practical introduction to launching a remote training job using the SageMaker SDK's 'Estimator' class.
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